Image segmentation through combined methods: Watershed transform, unsupervised distance learning and normalized cut

Página do item simplificado
dc.contributor.authorPinto, Tiago W.
dc.contributor.authorDe Carvalho, Marco A. G.
dc.contributor.authorPedronette, Daniel C. G. [UNESP]
dc.contributor.authorMartins, Paulo S.
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T16:56:09Z
dc.date.available2018-12-11T16:56:09Z
dc.date.issued2014-01-01
dc.description.abstractResearch on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance. © 2014 IEEE.en
dc.description.affiliationSchool of Technology, UNICAMP, Limeira - 13484-332, São Paulo
dc.description.affiliationDepartment of Statistics, Applied Mathematics and Computing, UNESP, Rio Claro - 13506-900, São Paulo
dc.description.affiliationUnespDepartment of Statistics, Applied Mathematics and Computing, UNESP, Rio Claro - 13506-900, São Paulo
dc.format.extent153-156
dc.identifierhttp://dx.doi.org/10.1109/SSIAI.2014.6806052
dc.identifier.citationProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, p. 153-156.
dc.identifier.doi10.1109/SSIAI.2014.6806052
dc.identifier.scopus2-s2.0-84902294375
dc.identifier.urihttp://hdl.handle.net/11449/171597
dc.language.isoeng
dc.relation.ispartofProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectgraph partitioning
dc.subjectimage segmentation
dc.subjectnormalized cut
dc.subjectunsupervised distance learning
dc.subjectwatershed transform
dc.titleImage segmentation through combined methods: Watershed transform, unsupervised distance learning and normalized cuten
dc.typeTrabalho apresentado em evento

Arquivos

Coleções

Artigos - Estatistica, Matemática Aplicada e Computação - IGCE